2.2k citations · 10.8k across the 93 of their papers we have counts for
15 papers · 1 filter
Few-shot Sequence Learning with Transformers
Lajanugen Logeswaran, Ann Lee, Myle Ott +3
Few-shot algorithms aim at learning new tasks provided only a handful of training examples. In this work we investigate few-shot learning in the setting where the data points are s…
Text-to-Image Generation Grounded by Fine-Grained User Attention
Jing Yu Koh, Jason Baldridge, Honglak Lee +1
Localized Narratives is a dataset with detailed natural language descriptions of images paired with mouse traces that provide a sparse, fine-grained visual grounding for phrases. W…
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning
Guangxiang Zhu, Minghao Zhang, Honglak Lee +1
Sample efficiency has been one of the major challenges for deep reinforcement learning. Recently, model-based reinforcement learning has been proposed to address this challenge by…
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment
Wilka Carvalho, Anthony Liang, Kimin Lee +4
First-person object-interaction tasks in high-fidelity, 3D, simulated environments such as the AI2Thor virtual home-environment pose significant sample-efficiency challenges for re…
i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
Kibok Lee, Yian Zhu, Kihyuk Sohn +3
Contrastive representation learning has shown to be effective to learn representations from unlabeled data. However, much progress has been made in vision domains relying on data a…
Text as Neural Operator: Image Manipulation by Text Instruction
Tianhao Zhang, Hung-Yu Tseng, Lu Jiang +3
In recent years, text-guided image manipulation has gained increasing attention in the multimedia and computer vision community. The input to conditional image generation has evolv…
Understanding and Diagnosing Vulnerability under Adversarial Attacks
Haizhong Zheng, Ziqi Zhang, Honglak Lee +1
Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks. Currently, there is no clear insight into how slight perturbations cause such a large difference in c…
Predictive Information Accelerates Learning in RL
Kuang-Huei Lee, Ian Fischer, Anthony Liu +4
The Predictive Information is the mutual information between the past and the future, I(X_past; X_future). We hypothesize that capturing the predictive information is useful in RL,…
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Kimin Lee, Younggyo Seo, Seunghyun Lee +2
Model-based reinforcement learning (RL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environment's dynamics. However, learning a global…
An Ode to an ODE
Krzysztof Choromanski, Jared Quincy Davis, Valerii Likhosherstov +6
We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d…
CompressNet: Generative Compression at Extremely Low Bitrates
Suraj Kiran Raman, Aditya Ramesh, Vijayakrishna Naganoor +3
Compressing images at extremely low bitrates (< 0.1 bpp) has always been a challenging task since the quality of reconstruction significantly reduces due to the strong imposed cons…
Time Dependence in Non-Autonomous Neural ODEs
Jared Quincy Davis, Krzysztof Choromanski, Jake Varley +6
Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform for…
Improved Consistency Regularization for GANs
Zhengli Zhao, Sameer Singh, Honglak Lee +3
Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator. We improve on this technique in several wa…
High-Fidelity Synthesis with Disentangled Representation
Wonkwang Lee, Donggyun Kim, Seunghoon Hong +1
Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models. Despite recent advances in disent…
Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
Sungryull Sohn, Hyunjae Woo, Jongwook Choi +1
We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph which describes a set of subtasks and their dependencies that are unknown to th…